Prompt-based methods, which encode medical priors through descriptive text, have been only minimally explored for CT Image Quality Assessment (IQA). While such prompts can embed prior knowledge about diagnostic quality, they often introduce bias by reflecting idealized definitions that may not hold under real-world degradations such as noise, motion artifacts, or scanner variability. To address this, we propose the Context-Aware Prompt-guided Image Quality Assessment (CAP-IQA) framework, which integrates text-level priors with instance-level context prompts and applies causal debiasing to separate idealized knowledge from factual, image-specific degradations. Our framework combines a CNN-based visual encoder with a domain-specific text encoder to assess diagnostic visibility, anatomical clarity, and noise perception in abdominal CT images. The model leverages radiology-style prompts and context-aware fusion to align semantic and perceptual representations. On the 2023 LDCTIQA challenge benchmark, CAP-IQA achieves an overall correlation score of 2.8590 (sum of PLCC, SROCC, and KROCC), surpassing the top-ranked leaderboard team (2.7427) by 4.24%. Moreover, our comprehensive ablation experiments confirm that prompt-guided fusion and the simplified encoder-only design jointly enhance feature alignment and interpretability. Furthermore, evaluation on an in-house dataset of 91,514 pediatric CT images demonstrates the true generalizability of CAP-IQA in assessing perceptual fidelity in a different patient population.

翻译：基于提示的方法通过描述性文本编码医学先验知识，在CT图像质量评估（IQA）领域的探索尚不充分。此类提示虽能嵌入关于诊断质量的先验知识，但常因反映理想化定义而引入偏差，这些定义在真实世界的退化（如噪声、运动伪影或扫描仪变异）下可能失效。为此，我们提出上下文感知提示引导图像质量评估（CAP-IQA）框架，该框架将文本级先验与实例级上下文提示相结合，并应用因果去偏技术以分离理想化知识与事实性图像特定退化。本框架结合基于CNN的视觉编码器与领域专用文本编码器，用于评估腹部CT图像的诊断可见性、解剖结构清晰度及噪声感知。模型利用放射学风格提示与上下文感知融合技术，实现语义表征与感知表征的对齐。在2023年LDCTIQA挑战基准测试中，CAP-IQA获得2.8590的综合相关性分数（PLCC、SROCC与KROCC之和），较排行榜首团队（2.7427）提升4.24%。此外，系统的消融实验证实：提示引导融合与简化的纯编码器设计共同提升了特征对齐能力与可解释性。进一步在包含91,514张儿科CT图像的内部数据集上的评估表明，CAP-IQA在不同患者群体中评估感知保真度具有真正的泛化能力。